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<table width="100%" summary="page for RockTheVote"><tr><td>RockTheVote</td><td align="right">R Documentation</td></tr></table>

<h2>Voter turnout experiment, using Rock The Vote ads</h2>

<h3>Description</h3>


<p>Voter turnout data spanning 85 cable TV systems, randomly allocated to
a voter mobilization experiment targetting 18-19 year olds with &quot;Rock
the Vote&quot; television advertisments 
</p>


<h3>Usage</h3>

<pre>data(RockTheVote)</pre>


<h3>Format</h3>


<p>A data frame with 85 observations on the following 6 variables.
</p>

<dl>
<dt><code>strata</code></dt><dd><p>numeric, experimental strata</p>
</dd>
<dt><code>treated</code></dt><dd><p>numeric, 1 if a treated cable system, 0 otherwise</p>
</dd>
<dt><code>r</code></dt><dd><p>numeric, number of 18 and 19 year olds turning out</p>
</dd>
<dt><code>n</code></dt><dd><p>numeric, number of 19 and 19 year olds registered</p>
</dd>
<dt><code>p</code></dt><dd><p>numeric, proportion of 18 and 19 year olds turning out</p>
</dd>
<dt><code>treatedIndex</code></dt><dd><p>numeric, a counter indexing the 42 treated units</p>
</dd>
</dl>



<h3>Details</h3>

<p>Green and Vavreck (2008) implemented a cluster-randomized
experimental design in assessing the effects of a voter mobilization
treatment in the 2004 U.S. Presidential election.  The clusters in this
design are geographic areas served by a single cable television system.
So as to facilitate analysis, the researchers restricted their
attention to small cable systems whose reach is limited to a single zip
code.  Further, since the experiment was fielded during the last week
of the presidential election, the researchers restricted their search
to cable systems that were not in the 16 hotly-contested
&ldquo;battleground&rdquo; states (as designated by the <EM>Los Angeles
Times</EM>).
</p>
<p>Eighty-five cable systems were available for randomization and were
assigned to treatment after stratification on previous turnout levels
in presidential elections (as determined from analysis of the
corresponding states' voter registration files).  Each cable system was
matched with one or sometimes two other cable systems in the same
state, yielding 40 strata.  Then within each strata, cable systems were
randomly assigned to treatment and control conditions.  Strata 3, 8 and
25 have two control cable systems and 1 treated system each, while
strata 6 and 20 have two treated cable systems and one control system.
The remaining 35 strata have 1 treated cable system and 1 control
system.  In this way there are 38 + 4 = 42 treated systems, spanning 40
experiment strata.
</p>
<p>The treatment involved researchers purchasing prime-time advertising
spots on four channels in the respective cable system in which the
researchers aired voter mobilization ads.  The ads were produced by
<EM>Rock the Vote</EM>, targeted at younger voters, and aired four times
per night, per channel, over the last eight days of the election
campaign.  After the election, public records were consulted to
assemble data on turnout levels in the treated and control cable
systems.  In the analysis reported in Green and Vavreck (2008), the
researchers focused on turnout among registered voters aged 18 and 19
years old.
</p>


<h3>References</h3>

<p>Green, Donald P. and Lynn Vavreck. 2008. Analysis of
Cluster-Randomized Experiments: A Comparison of Alternative Estimation
Approaches. <EM>Political Analysis</EM> 16:138-152.
</p>
<p>Jackman, Simon, 2009. <EM>Bayesian Analysis for the Social Sciences</EM>.  
Wiley: Hoboken, New Jersey.  Example 7.9.
</p>


<h3>Examples</h3>

<pre>
data(RockTheVote)
## estimate MLEs of treatment effects
deltaFunction &lt;- function(data){
  model &lt;- glm(cbind(r,n-r)~treated,
               data=data,
               family=binomial)
  c(coef(model)[2],
    confint(model)[2,])
}


tmp &lt;- by(RockTheVote,
          as.factor(RockTheVote$strata),
          deltaFunction)

tmp &lt;- matrix(unlist(tmp),ncol=3,byrow=TRUE)

indx &lt;- order(tmp[,1])

plot(y=1:40,
     x=tmp[indx,1],
     pch=16,cex=1.25,
     xlim=range(tmp),
     ylab="",
     axes=FALSE,
     xlab="Estimated Treatment Effect (MLEs, Logit Scale)")
text(y=1:40,
     x=par()$usr[1],
     pos=4,
     as.character((1:40)[indx]),
     cex=.5)
segments(x0=tmp[indx,2],
         x1=tmp[indx,3],
         y0=1:40,
         y1=1:40)
axis(1)
axis(3)
abline(v=0)
</pre>


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